DEV Community: Diana Wachenje

Understanding Parametric and Non-Parametric Tests Made Simple

Diana Wachenje — Tue, 07 Jul 2026 20:52:15 +0000

Introduction

Parametric and non-parametric statistical tests are used to analyze data. The main difference is whether the test made assumes the data follow a particular distribution usually known as normal distribution or not.

Definition

1). Parametric tests
Parametric tests are statistical tests used to analyze numerical data and also make assumptions about the population on data that has been collected.

An example: Suppose a researcher wants to know whether a new drug lowers blood pressure more effectively than an old drug.

The blood pressure readings (121,128,135,140 mmHg). These are continuous numerical data. The readings are normally distributed by the researcher through a parametric test known as the independent test to compare the average pressure between the two groups.

2). Non-parametric tests
Non-parametric tests are statistical tests that do not require to follow a specific distribution such as normal distribution. They are used when assumptions for parametric tests are not met.
Example: A doctor compares patients' pain ratings before and after receiving physiotherapy.

Differences between parametric and non-parametric tests.

1). Parametric tests compare means (averages) whereas non-parametric tests compare median, ranks, or frequencies.

2). Parametric tests are more powerful when assumptions are met whereas non-parametric tests are powerful but more robust when assumptions are violated.

3). Parametric tests are more sensitive to extreme outliers whereas non-parametric are less sensitive to outliers because many tests use rank instead raw values.

4). Parametric tests often perform best with moderate or large samples whereas non-parametric are well suited with small samples.

5). Parametric tests assume that the data follows a specific distribution (normal) whereas non-parametric tests do not require the data to follow a specific distribution.

Use of parametric and non-parametric tests and their role in data science.

1). Use of Parametric Tests in Data Science

a). Hypothesis testing.
b). Predictive analytics.
c). Measuring relationships among variables.
d). Evaluating machine learning models using statical comparisons.

2). Uses of Non-Parametric Tests in Data Science

a). Analyzing survey and questionnaire data.
b). Working with non-normal or incomplete datasets.
c). Detecting associations in categorical variables.
d). Validating results when parameters assumptions are not met.

In conclusion parametric and non-parametric tests enable data scientists to choose appropriate analytical methods for different types of data leading to more reliable concluding and better-informed decisions.

STATISTICS MATTERS IN DATA SCIENCE

Diana Wachenje — Wed, 24 Jun 2026 22:18:34 +0000

Introduction

Statistics is a branch of applied mathematics that helps one to collect, organize, analyze, interpret and present data.
Its primary goal is to extract meaningful insights from numerical facts and figures to understand trends, summarize information and make information and make informed decisions in the face of uncertainty.

Statistical and non-statistical analysis

This can be done in two ways;

1). Statistical analysis
This is used to collect, explore and present large amounts of data to identify patterns and trends. It is also called quantitative analysis.
2). Non statistical analysis
It provides generic information and includes text, sound, still images and moving images. It is also called qualitative analysis.

Major categories of statistics

1). Descriptive statistics
It helps organize data and focuses on the main characteristics of the data. It also provides a summary of the data numerically or graphically.
. Examples of descriptive statistics;
. Mean (average)
. Median
. Mode
. Range
. Standard deviation
An example; Finding the average score of students in a class.

2). Inferential statistics
It is used to make predictions and conclusions about a population based on a sample. It also helps in decision and forecasting.
. Examples of inferential statistics;
. Probability testing
. Regression analysis
. Hypothesis making
. Confidence intervals
An example in a polling station a sample of 2000 voters to predict the outcome of a national election.

Types of Data

What is data?
This is a collection of raw fact values measurements collected for analysis. The main types of data are;

1). Qualitative Data (Categorical Data)
This is the type of data that describes qualities or characteristics and cannot be measured numerically.

 Types of Qualitative Data

. Nominal Data
This is data grouped into categories with no specific order.
. Examples of nominal data;
. Gender
. Religion
. Eye color
. Ordinal Data
This is data grouped into categories that have an order or ranking.
. Examples of ordinal data;
. Customer satisfaction (poor, good, excellent)
. Class positions
. Education level

2). Quantitative Data (Numerical Data)
This is the type of data that consists of numbers and can be measured or counted.

Types of Quantitative Data

    . Discrete Data

These are countable numbers, usually whole numbers.
. Examples of discrete data;
. Number of students
. Number of cars
. Goal scored
. Continuous Data
This is data that has measurable values which take any value within a range.
. Examples of continuous data;
. Height
. Weight
. Temperature

Statistical Data analysis steps

1). Define the problem
2). Collect data
3). Organize and clean the data
4). Explore the data
5). Choose the appropriate statistical method
6). Analyze the data
7). Interpret Results
8). Draw conclusions and make decisions
9). Present the findings

Common mistakes

1). Using poor quality data
2). Confusing correlation with causation
3). Misinterpreting Probability
4). Data leaking
5). Poor data visualization

Conclusion
Without statistics, data scientists would find it hard to analyze data science models which will lead to lack of accuracy, reliability, and scientific validity.

# A BEGINNER'S JOURNEY INTO PANDAS

Diana Wachenje — Sun, 21 Jun 2026 21:33:28 +0000

Introduction

Pandas is an open-source library used in python for data cleaning, manipulation, analysis and visualization. It allows users to work with structured data that is tables, spreadsheets and databases efficiently.
The name Pandas came from "Panel Data". It is a term used in statistics and economics.

Importance of Pandas

Organizations produce huge amounts of data every day. Pandas helps professionals to;

. Clean, messy data
. Analyze trends and patterns
. Calculate statistics
. Merge datasets
. Prepare data for machine learning and visualization

Main Data Structures in Pandas

1). Series
A series is like a column in a table. It is a one-dimensional array holding data of any type.

Example:

Python

import pandas as pd
age=pd. Series ([15, 25, 35,40])
print(ages)

Output
Plain text
0 15
1 25
2 35
3 40

2). Data Frame
A Data Frame is a two-dimensional data structure, like two-dimensional array, a table with rows and columns.

Example:

Python

import pandas as pd

lecturers: {

"Name": ["Harun", "Bridgit", "Navas", "Emmanuel"]
"Age": [30,24, 22, 25]
}
df=pd.DataFrame(lecturers)
print(df)

Output

Plain text
Name Age
0 Harun 30
1 Bridgit 24
2 Navas 22
3 Emmanuel 25

Real-Life Uses of Pandas

1). Education
Colleges and schools are able to analyze student performances.

Example:
Python
top_students=df[df["score"]>=80]
print(top_students)

2). Data Science and Machine Learning
Data Scientists are able to clean data, analyze and prepare the data before building new models.

Example:

Python
df.dropna(inplace=True)
This removes missing values from the dataset.

3). Healthcare
Hospitals are able to analyze the number of patients records and also improve the services.

Example:
Python
average_age=patients["age"]mean()
print(average_age)

Healthcare professionals are able to study diseases patterns and patient demographics.

4). Business and Sales Analysis
Companies are able to analyze sales performance and make decisions.

Example:
Python
sales=pd.read_csv("sales.csv")
total_sales=sales["amount"].sum()
print(total_sales)

This helps business to determine:
. Best-selling products
. Total revenue
. Monthly sales trends

Common Errors in Pandas

While working with Pandas we often make these mistakes;

1). Forgetting to import pandas
Python
df=pd.read_csv("data.csv")
Name error: Name 'pd' is not defined

2). Forgetting Parenthesis with Methods
3). Confusing loc and iloc
. Loc uses labels (names)
. Iloc uses positions(numbers)

# Conclusion

Pandas is one of the most powerful libraries in Python with data.
It is commonly used in healthcare, finance, business, education and data science to clean, analyze and interpret data for better decision making. It is very essential for someone learning data science and data analysis.

How to Publish a Power BI Report and Embed it into a website

Diana Wachenje — Sun, 05 Apr 2026 07:00:50 +0000

A brief introduction of Power BI and Publishing Process

Power BI is a business tool that has been developed for analysis by Microsoft to allow user visualization of data, create interactive reports and share some insights across an organization or company. Whereas publishing process is just sharing your report so that others can have access to it. So far learnt about Power BI Queries, DAX, data modelling, joins, charts, dashboards and reporting.

Power BI consists of three main components:

Power BI Desktop - It is used to build reports and data models.
Power BI Service - This is a cloud-based platform which is used to share, collaborate and one is able to access reports online.
Power BI Mobile - These are just apps in the smartphones or tablets used for viewing dashboards.

Here are few steps to follow while creating workspace

1). Create a Report

Ensure you have built your report in Power BI Desktop by using visuals like charts, tables and maps.

2). Save the File

Always ensure your work is saved in (pbix format).

3). Sign In

Log into your Power BI account (Usually through an organization or Microsoft Account). Examples: datascience@luxdevhq.com or johndoe@gmail.com

4). Publish the Report

Click the publish button, a shade of blue at the far end on the left written Publish.
Choose a destination Workspace in Power BI Service this can either be Shared Workspace for an organization or My Workspace for personal use.

5). Access Power BI Service

Your report will be uploaded on cloud where it can be viewed in a browser.

6). Share and Collaborate

Once the report is published you can share with colleagues via links, apps or workspace access.
You can also restrict your work by choosing to make it public or private, choosing who can or cannot see it.

7). Schedule Refresh

This is optional, you can decide to refresh your report so that it can be up to date.

UNDERSTANDING DATA MODELLING IN POWER BI

Diana Wachenje — Mon, 30 Mar 2026 22:04:58 +0000

Introduction

Power BI is a business analytic platform that transforms raw data into actionable insights through visualizations and reports that helps users to analyze data, identify trends securely across organizations to make data-driven decisions.
Data modelling is the process to connect, structure, optimize data tables to work efficiently together.

Concepts to Understand

Joins
Joins are performed in Power Query where you combine two columns by selecting the first and the second table and join them by clicking on the headers. Also ensure they have same data type for accurate matching.
Relationships
Relationships define how table connect. These are,
One-to-Many (Most common)
Many-to-One
Many-to- Many (Used carefully)
Example:
One customer- Many Sales
Schemas
Schemas refers to the way your data is structured either in facts tables or dimensions and organized in the data model.
One Central fact table
Multiple surrounding dimension tables

Power BI Relationships

Cardinality Cardinality is how rows relate:
One-to- One
One-to- Many
Many-to- Many
Cross Filter Direction
It controls how filters move between tables:
Single direction
Both directions

Commonly modelling issues

Not using proper Data Table
Mixing fact and dimensional data
Creating too many many-to-many relationships

Steps-by-steps Data modeling process

Step 1: Load Data
Import from Excel, SQL etc.
Step 2: Clean Data (Power Query)
Fix structure before modelling
Step 3: Create Relationships
Go to model view and connect tables
Step 4: Build Star Schema
Organize tables properly
Step 5: Create measures (DAX)
Add business logic
Step 6: Optimize Model

Remove unused columns
Use correct data types
Avoid unnecessary relationships

# Simple Real-life Examples

In a mini mart setup:

Sales table- transactions
Product table- item details
Customer table- buyer information

## Why Data Modelling is important

Faster reports
Accurate Insights -Easier Dashboards

HOW EXCEL IS USED IN REAL -WORLD ANALYSIS

Diana Wachenje — Sun, 29 Mar 2026 18:28:58 +0000

Microsoft Excel

Microsoft Excel is a spreadsheet that has been designed for data entry methods, basic formula creation, data sorting and filtering, basic charts and visualizations and file management. Through Excel one gets to identify the knowledge level, explore specific features of interests and applies it through practical examples.

How Excel is used in Real World Scenarios

Personal and project management Excel is used for personal finances an individual tracks their budgets, calculate expenses and visualize spending trends with charts. Whereas students; it helps them analyze academic performance, for researching data and organizing course schedules.
Business and Financial Application Excel is used widely in business for organizing data, creating budget forecasts, analyze revenue trends, manage cash flows data analysis and reporting, operational and resource management, administrative and scheduling, inventory and pharmacy management, financial and billing, patient monitoring and research to track the performance.

Examples of features and formulas

Excel has different functions which are used in the worksheet to perform calculations in the cell values provided.

Basic Functions

SUM, AVERAGE, COUNT, MAX, MIN
Mathematical operations
Text manipulation functions
Date and time functions

Intermediate Functions

VLOOKUP, HLOOKUP, INDEX, MATCH -Logical functions (IF, AND, OR, NOT) -Error handling (IFERROR, IFNA)
COUNTIF, SUMIF and the variants

Advanced Functions

Array formulas
Financial functions (NPV, PMT)
Statical analysis function
Database functions
Power Query introduction

# Data analysis Features

PivotTables and Pivot Charts
Data validation techniques
Conditional formatting strategies
What-if analysis tools
Dashboard creation techniques